Organizations are industrializing their DSML initiatives through increased automation and improved access to ML artefacts, and by accelerating the journey from proof of concept to production. Data and analytics leaders should use this report to understand key trends and innovations.
- What You Need to Know
- The Hype Cycle
- The Priority Matrix
- Off the Hype Cycle
- On the Rise
- Quantum ML
- Self-Supervised Learning
- Generative Adversarial Networks
- Differential Privacy
- Federated Machine Learning
- Adaptive ML
- Reinforcement Learning
- Transfer Learning
- Synthetic Data
- At the Peak
- Decision Intelligence
- Large-Scale Pretrained Language Model
- AI-Related C&SI Services
- Data Labeling and Annotation Services
- Explainable AI
- Augmented DSML
- Citizen Data Science
- Deep Neural Networks (Deep Learning)
- Prescriptive Analytics
- Sliding Into the Trough
- Graph Analytics
- Advanced Video/Image Analytics
- Event Stream Processing
- Climbing the Slope
- Predictive Analytics
- Text Analytics
- Entering the Plateau
- Hype Cycle Phases, Benefit Ratings and Maturity Levels
Gartner Recommended Reading
©2020 Gartner, Inc. and/or its affiliates.
All rights reserved.
Gartner is a registered trademark of Gartner, Inc. and its affiliates.
This publication may not be reproduced or distributed in any form without Gartner’s prior written permission.
It consists of the opinions of Gartner’s research organization, which should not be construed as statements of fact.
While the information contained in this publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information.
Although Gartner research may address legal and financial issues, Gartner does not provide legal or investment advice and its research should not be construed or used as such.
Your access and use of this publication are governed by Gartner’s Usage Policy.
Gartner prides itself on its reputation for independence and objectivity.
Its research is produced independently by its research organization without input or influence from any third party.
For further information, see
Guiding Principles on Independence and Objectivity.